Prediction of Estimated Times of Arrival (ETA) is a challenging problem for the aviation industry. Flights recurrently deviate from their scheduled time of arrival, which has negative downstream consequences that affect the efficiency of operations. Therefore, accurate and up-to-date ETA estimations prior to its landing can help in optimizing the actions to be taken by the different air transportation agents whenever schedule deviations are incurred, and thus reduce the economic, logistic and environmental impact that they cause. This presentation exposes an infrastructure for high-fidelity computation of accurate ETA in real-time, based on a data-driven approach that leverages the use of recorded aircraft trajectories. This infrastructure is composed of different elements: (1) a live ADS-B tracks gathering system embedded in a lambda-architecture cluster, with capabilities for real-time distribution and data lake storage (2) an ETA prediction machine learning model, employing the actual 4D aircraft position as input; and (3) a hybrid cloud architecture to support real-time visualization and distributions of ETA predictions. The proposed infrastructure has been successfully validated in a real environment (Transforming Transport, an European Commission funded project). This infrastructure enables real-time computation and distribution of accurate ETA for any arrival operation of interest. Results supported the envisioned benefits of getting such accurate ETA, which basically turn into a reduction of associated costs for airport authorities and airlines.

In movement data analysis, there exists a problem of comparing multiple trajectories of moving objects to common or distinct reference trajectories. We introduce a general conceptual framework for comparative analysis of trajectories and an analytical procedure, which consists of (1) finding corresponding points in pairs of trajectories, (2) computation of pairwise difference measures, and (3) interactive visual analysis of the distributions of the differences with respect to space, time, set of moving objects, trajectory structures, and spatio-temporal context. We propose a combination of visualisation, interaction, and data transformation techniques supporting the analysis and demonstrate the use of our approach for solving a challenging problem from the aviation domain.

We introduce the concept of time mask, which is a type of temporal filter suitable for selection of multiple disjoint time intervals in which some query conditions fulfil. Such a filter can be applied to time-referenced objects, such as events and trajectories, for selecting those objects or segments of trajectories that fit in one of the selected time intervals. The selected subsets of objects or segments are dynamically summarized in various ways, and the summaries are represented visually on maps and/or other displays to enable exploration. The time mask filtering can be especially helpful in analysis of disparate data (e.g., event records, positions of moving objects, and time series of measurements), which may come from different sources. To detect relationships between such data, the analyst may set query conditions on the basis of one dataset and investigate the subsets of objects and values in the other datasets that co-occurred in time with these conditions. We describe the desired features of an interactive tool for time mask filtering and present a possible implementation of such a tool. By example of analysing two real world data collections related to aviation and maritime traffic, we show the way of using time masks in combination with other types of filters and demonstrate the utility of the time mask filtering. Keywords: Data visualization, Interactive visualization, Interaction technique